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In this paper, we consider the problem of machine reading task when thequestions are in the form of keywords, rather than natural language. In recentyears, researchers have achieved significant success on machine readingcomprehension tasks, such as SQuAD and TriviaQA. These datasets provide anatural language question sentence and a pre-selected passage, and the goal isto answer the question according to the passage. However, in the situation ofinteracting with machines by means of text, people are more likely to raise aquery in form of several keywords rather than a complete sentence. Thekeyword-based query comprehension is a new challenge, because small variationsto a question may completely change its semantical information, thus yielddifferent answers. In this paper, we propose a novel neural network system thatconsists a Demand Optimization Model based on a passage-attention neuralmachine translation and a Reader Model that can find the answer given theoptimized question. The Demand Optimization Model optimizes the original queryand output multiple reconstructed questions, then the Reader Model takes thenew questions as input and locate the answers from the passage. To makepredictions robust, an evaluation mechanism will score the reconstructedquestions so the final answer strike a good balance between the quality of boththe Demand Optimization Model and the Reader Model. Experimental results onseveral datasets show that our framework significantly improves multiple strongbaselines on this challenging task.

Keyword-based Query Comprehending via Multiple Optimized-Demand Augmentation